library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 3.6.2
## Warning: package 'tibble' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'readr' was built under R version 3.6.2
## Warning: package 'purrr' was built under R version 3.6.2
## Warning: package 'dplyr' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(readr)
library(coefplot)
Homeowner_data <- read_csv("~/Desktop/Econometrics/Homeowner.data.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   AGE_REF_ = col_character(),
##   AGE2_ = col_character(),
##   CUTE_URE = col_character(),
##   DESCRIP_ = col_character(),
##   EARN_OMP = col_character(),
##   EDUC0REF = col_character(),
##   EDUCA2_ = col_character(),
##   EMPL_YP1 = col_character(),
##   EMPL_YP2 = col_character(),
##   FAM__IZE = col_character(),
##   FAM__YPE = col_character(),
##   FGVX_ = col_character(),
##   FINC_EFX = col_character(),
##   FIRAX_ = col_character(),
##   FJSS_EDX = col_character(),
##   FPVTX_ = col_character(),
##   FREEMLX_ = col_character(),
##   FRRX_ = col_character(),
##   FS_MTHI_ = col_character(),
##   FSS_RRX_ = col_character()
##   # ... with 78 more columns
## )
## ℹ Use `spec()` for the full column specifications.
## Warning: 36 parsing failures.
##  row      col           expected actual                                        file
## 3798 ROYESTB  1/0/T/F/TRUE/FALSE  12    '~/Desktop/Econometrics/Homeowner.data.csv'
## 3798 ROYESTBX 1/0/T/F/TRUE/FALSE  60000 '~/Desktop/Econometrics/Homeowner.data.csv'
## 3799 ROYESTB  1/0/T/F/TRUE/FALSE  12    '~/Desktop/Econometrics/Homeowner.data.csv'
## 3799 ROYESTBX 1/0/T/F/TRUE/FALSE  60000 '~/Desktop/Econometrics/Homeowner.data.csv'
## 4233 ROYESTB  1/0/T/F/TRUE/FALSE  8     '~/Desktop/Econometrics/Homeowner.data.csv'
## .... ........ .................. ...... ...........................................
## See problems(...) for more details.
#The code below creates my subset of the data which is between and including the ages of 20 and 87 with a family size of at least 3.
#I also setup RACE, HOMEOWNERSHIP, MARITAL STATUS, & EDUC level as factors (some ordered).

Homeowner_data$REF_RACE <- as.factor(Homeowner_data$REF_RACE)
levels(Homeowner_data$REF_RACE) <- c("White", "Black", "Native American", "Asian", "Pacific Islander", "Multi Race")
Homeowner_data$CUTENURE <- as.factor(Homeowner_data$CUTENURE)
levels(Homeowner_data$CUTENURE) <- c("Mortgage","No Mortgage","Mortgage Status Not Reported", "Renter", "Occupied Without Payment Of Cash", "Student Housing")
Homeowner_data$EDUC_REF <- as.factor(Homeowner_data$EDUC_REF)
levels(Homeowner_data$EDUC_REF) <- c("Never Attended","Grade 8","High School No Degree", "High School", "Some College", "Associates", "Bachelors", "Masters or PHD")
Homeowner_data$MARITAL1 <- as.factor(Homeowner_data$MARITAL1)
levels(Homeowner_data$MARITAL1) <- c("Married","Widowed","Divorced", "Seperated", "Never Married")

use_varb <- (Homeowner_data$AGE_REF >= 20) & (Homeowner_data$AGE_REF <= 87) & (Homeowner_data$FAM_SIZE >= 3)
dat_use <- subset(Homeowner_data,use_varb) 
#The regression below attempts to find a relationship between yearly wages and several variables in order to find what variables in the data set would be good predictors for a male homeowner in my subset.

model_temp1 <- lm(FWAGEXM ~ AGE_REF + EDUC_REF + REF_RACE + CUTENURE + SEX_REF,  data = dat_use)  
summary(model_temp1)
## 
## Call:
## lm(formula = FWAGEXM ~ AGE_REF + EDUC_REF + REF_RACE + CUTENURE + 
##     SEX_REF, data = dat_use)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -177426  -41545  -10907   26402  525304 
## 
## Coefficients:
##                                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               97041.90   18145.25   5.348 9.43e-08
## AGE_REF                                     180.09      97.64   1.844  0.06520
## EDUC_REFGrade 8                          -18862.21   18330.06  -1.029  0.30353
## EDUC_REFHigh School No Degree             -7674.52   17774.13  -0.432  0.66593
## EDUC_REFHigh School                        1098.07   17420.02   0.063  0.94974
## EDUC_REFSome College                       6979.04   17439.30   0.400  0.68904
## EDUC_REFAssociates                        11213.93   17602.20   0.637  0.52411
## EDUC_REFBachelors                         49129.13   17424.48   2.820  0.00483
## EDUC_REFMasters or PHD                    75711.93   17528.82   4.319 1.61e-05
## REF_RACEBlack                            -11959.95    4180.51  -2.861  0.00425
## REF_RACENative American                   -5325.03   11739.80  -0.454  0.65015
## REF_RACEAsian                              6543.54    4249.44   1.540  0.12368
## REF_RACEPacific Islander                   4959.27   16384.94   0.303  0.76216
## REF_RACEMulti Race                        21443.21    7785.16   2.754  0.00591
## CUTENURENo Mortgage                      -36966.78    3600.33 -10.268  < 2e-16
## CUTENUREMortgage Status Not Reported     -16900.06   10300.98  -1.641  0.10096
## CUTENURERenter                           -40109.96    2793.49 -14.358  < 2e-16
## CUTENUREOccupied Without Payment Of Cash -39552.95   12313.63  -3.212  0.00133
## SEX_REF                                  -10335.62    2302.86  -4.488 7.40e-06
##                                             
## (Intercept)                              ***
## AGE_REF                                  .  
## EDUC_REFGrade 8                             
## EDUC_REFHigh School No Degree               
## EDUC_REFHigh School                         
## EDUC_REFSome College                        
## EDUC_REFAssociates                          
## EDUC_REFBachelors                        ** 
## EDUC_REFMasters or PHD                   ***
## REF_RACEBlack                            ** 
## REF_RACENative American                     
## REF_RACEAsian                               
## REF_RACEPacific Islander                    
## REF_RACEMulti Race                       ** 
## CUTENURENo Mortgage                      ***
## CUTENUREMortgage Status Not Reported        
## CUTENURERenter                           ***
## CUTENUREOccupied Without Payment Of Cash ** 
## SEX_REF                                  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 68830 on 3715 degrees of freedom
## Multiple R-squared:  0.255,  Adjusted R-squared:  0.2514 
## F-statistic: 70.66 on 18 and 3715 DF,  p-value: < 2.2e-16
plot(model_temp1)

require(stargazer)
## Loading required package: stargazer
## 
## Please cite as:
##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
stargazer(model_temp1, type = "default")
## 
## % Error: 'style' must be either 'latex' (default), 'html' or 'text.'
# The strong predictors for male homeownership seems to be wages, education, race. When family size is added to the subset the wages for both asian male and black male homeowners the slope becomes positive. Do extra family members persuade companies to pay more in wages? Something to think about.

reg_biv <- lm(FWAGEXM ~ AGE_REF + REF_RACE + EDUC_REF, data = dat_use)  

age_35_bachelors <- coef(reg_biv)[1] + 35*coef(reg_biv)[2] + coef(reg_biv)[3] + coef(reg_biv)[4]

summary(reg_biv)
## 
## Call:
## lm(formula = FWAGEXM ~ AGE_REF + REF_RACE + EDUC_REF, data = dat_use)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -177694  -44356  -10379   28879  530036 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    47864.7    18292.0   2.617 0.008914 ** 
## AGE_REF                          306.8       95.7   3.206 0.001356 ** 
## REF_RACEBlack                 -18502.4     4297.1  -4.306 1.71e-05 ***
## REF_RACENative American       -13787.7    12163.4  -1.134 0.257061    
## REF_RACEAsian                   2379.3     4385.5   0.543 0.587470    
## REF_RACEPacific Islander      -13840.7    16954.4  -0.816 0.414355    
## REF_RACEMulti Race             16863.2     8064.4   2.091 0.036590 *  
## EDUC_REFGrade 8               -20237.8    19009.8  -1.065 0.287126    
## EDUC_REFHigh School No Degree  -6600.3    18432.9  -0.358 0.720309    
## EDUC_REFHigh School             7322.9    18055.3   0.406 0.685073    
## EDUC_REFSome College           18883.2    18054.8   1.046 0.295684    
## EDUC_REFAssociates             25387.5    18217.2   1.394 0.163520    
## EDUC_REFBachelors              65658.5    18021.9   3.643 0.000273 ***
## EDUC_REFMasters or PHD         93021.1    18129.0   5.131 3.03e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 71410 on 3720 degrees of freedom
## Multiple R-squared:  0.197,  Adjusted R-squared:  0.1942 
## F-statistic:  70.2 on 13 and 3720 DF,  p-value: < 2.2e-16
print("Mean Wage of Age 35 person in my subset")
## [1] "Mean Wage of Age 35 person in my subset"
print(age_35_bachelors)
## (Intercept) 
##       26314
#Predicting the peak wage age of a black male homeowner with a bachelors degree.

NNobs <- length(dat_use$FWAGEXM)
set.seed(12345) 
graph_obs <- (runif(NNobs) < 0.1)
dat_graph <-subset(dat_use,graph_obs)

plot(FWAGEXM ~ jitter(AGE_REF, factor = 2), pch = 16, col = rgb(1, 0.2, 0.6, alpha = 0.2), main = "Wage vs Age for a black female homeowner with a bachelors degree", xlab = "Age", ylab = "Wage", ylim = c(40000,150000), data = dat_graph)

to_be_predicted2 <- data.frame(AGE_REF = 20:87, REF_RACE = "Black", EDUC_REF = "Bachelors", CUTENURE = "Mortgage", CUTENURE = "No Mortgage", CUTENURE = "Mortgage Status Not Reported", SEX_REF = 2)
to_be_predicted2$yhat <- predict(model_temp1, newdata = to_be_predicted2)

lines(yhat ~ AGE_REF, data = to_be_predicted2)

# For a black male homeowner with a bachelors degree, yearly wage starts at around $130,000 at age 20 and decreases at all ages from 20 to 80.
#Predicting the peak wage age of an asian male homeowner with a bachelors degree.

NNobs <- length(dat_use$FWAGEXM)
set.seed(12345) 
graph_obs <- (runif(NNobs) < 0.1)
dat_graph <-subset(dat_use,graph_obs)

plot(FWAGEXM ~ jitter(AGE_REF, factor = 2), pch = 16, col = rgb(1, 0.2, 0.6, alpha = 0.2), main = "Wage vs Age for an asian female homeowner with a bachelors degree", xlab = "Age", ylab = "Wage", ylim = c(80000,170000), data = dat_graph)

to_be_predicted2 <- data.frame(AGE_REF = 20:87, REF_RACE = "Asian", EDUC_REF = "Bachelors", CUTENURE = "Mortgage", CUTENURE = "No Mortgage", CUTENURE = "Mortgage Status Not Reported", SEX_REF = 2)
to_be_predicted2$yhat <- predict(model_temp1, newdata = to_be_predicted2)

lines(yhat ~ AGE_REF, data = to_be_predicted2)

# For an asian male homeowner with a bachelors degree, yearly wage starts at around $170,000 at age 20 and decreases at all ages from 20 to 80 but the slope is not as high (negatively) compared to the black male.

#This is interesting because it shows that black men are making less wages at all ages vs asian men with the same college degree.

#When family size is added to the subset the graph for wages for both asian male and black male homeowners has a positive slope. Do extra family members persuade companies to pay more in wages? Maybe family men are more likely to strive for higher wages? Something to think about.
# taking the log of the wage function allows for comparing values using percent changes and reducing the effect of education on wage. 

model_temp3 <- lm(log1p(FWAGEXM) ~ AGE_REF + REF_RACE + EDUC_REF + CUTENURE + MARITAL1 + SEX_REF, data = dat_use) 
summary(model_temp3)
## 
## Call:
## lm(formula = log1p(FWAGEXM) ~ AGE_REF + REF_RACE + EDUC_REF + 
##     CUTENURE + MARITAL1 + SEX_REF, data = dat_use)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -11.9607  -0.1657   0.3955   0.9879   3.8799 
## 
## Coefficients:
##                                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              12.922529   0.613073  21.078  < 2e-16
## AGE_REF                                  -0.024107   0.003446  -6.996 3.10e-12
## REF_RACEBlack                            -0.108472   0.141792  -0.765 0.444317
## REF_RACENative American                  -0.913539   0.395759  -2.308 0.021036
## REF_RACEAsian                            -0.006162   0.143133  -0.043 0.965661
## REF_RACEPacific Islander                 -1.554725   0.551746  -2.818 0.004861
## REF_RACEMulti Race                        0.337784   0.262248   1.288 0.197815
## EDUC_REFGrade 8                          -1.349111   0.616942  -2.187 0.028822
## EDUC_REFHigh School No Degree            -1.009188   0.597265  -1.690 0.091173
## EDUC_REFHigh School                      -0.569652   0.586017  -0.972 0.331076
## EDUC_REFSome College                     -0.497554   0.586568  -0.848 0.396356
## EDUC_REFAssociates                       -0.108913   0.592013  -0.184 0.854047
## EDUC_REFBachelors                         0.124732   0.586284   0.213 0.831534
## EDUC_REFMasters or PHD                    0.450917   0.589770   0.765 0.444580
## CUTENURENo Mortgage                      -1.018139   0.121578  -8.374  < 2e-16
## CUTENUREMortgage Status Not Reported     -0.561157   0.346200  -1.621 0.105124
## CUTENURERenter                           -0.685695   0.095480  -7.182 8.29e-13
## CUTENUREOccupied Without Payment Of Cash -0.116183   0.414628  -0.280 0.779332
## MARITAL1Widowed                          -0.672777   0.258321  -2.604 0.009240
## MARITAL1Divorced                         -0.340558   0.139863  -2.435 0.014941
## MARITAL1Seperated                        -0.550229   0.241333  -2.280 0.022667
## MARITAL1Never Married                    -0.753885   0.134014  -5.625 1.99e-08
## SEX_REF                                  -0.273525   0.078652  -3.478 0.000512
##                                             
## (Intercept)                              ***
## AGE_REF                                  ***
## REF_RACEBlack                               
## REF_RACENative American                  *  
## REF_RACEAsian                               
## REF_RACEPacific Islander                 ** 
## REF_RACEMulti Race                          
## EDUC_REFGrade 8                          *  
## EDUC_REFHigh School No Degree            .  
## EDUC_REFHigh School                         
## EDUC_REFSome College                        
## EDUC_REFAssociates                          
## EDUC_REFBachelors                           
## EDUC_REFMasters or PHD                      
## CUTENURENo Mortgage                      ***
## CUTENUREMortgage Status Not Reported        
## CUTENURERenter                           ***
## CUTENUREOccupied Without Payment Of Cash    
## MARITAL1Widowed                          ** 
## MARITAL1Divorced                         *  
## MARITAL1Seperated                        *  
## MARITAL1Never Married                    ***
## SEX_REF                                  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.312 on 3711 degrees of freedom
## Multiple R-squared:  0.1232, Adjusted R-squared:  0.118 
## F-statistic:  23.7 on 22 and 3711 DF,  p-value: < 2.2e-16
plot(model_temp3)

require(stargazer)
stargazer(model_temp3, type = "default")
## 
## % Error: 'style' must be either 'latex' (default), 'html' or 'text.'
REF_RACEB <- factor(c("0", "1", "0", "0", "0", "0"))
as.logical(as.integer(levels(REF_RACEB)[REF_RACEB]))
## [1] FALSE  TRUE FALSE FALSE FALSE FALSE
as.logical(as.integer(REF_RACEB) - 1L)
## [1] FALSE  TRUE FALSE FALSE FALSE FALSE
as.logical(as.integer(as.character(REF_RACEB)))
## [1] FALSE  TRUE FALSE FALSE FALSE FALSE
as.logical(REF_RACEB)
## [1] NA NA NA NA NA NA
levels(REF_RACEB) <- c(FALSE,TRUE)
REF_RACEB <- as.logical(REF_RACEB)
na.omit(REF_RACEB)
## [1] FALSE  TRUE FALSE FALSE FALSE FALSE
SEX_REFB <- factor(c("0", "1"))
as.logical(as.integer(levels(SEX_REFB)[SEX_REFB]))
## [1] FALSE  TRUE
as.logical(as.integer(SEX_REFB) - 1L)
## [1] FALSE  TRUE
as.logical(as.integer(as.character(SEX_REFB)))
## [1] FALSE  TRUE
levels(SEX_REFB) <- c(FALSE,TRUE)
SEX_REFB <- as.logical(SEX_REFB)
na.omit(SEX_REFB)
## [1] FALSE  TRUE
EDUC_REFB <- factor(c("0", "0", "0", "1", "0", "0", "0", "0"))
as.logical(as.integer(levels(EDUC_REFB)[EDUC_REFB]))
## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
as.logical(as.integer(EDUC_REFB) - 1L)
## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
as.logical(as.integer(as.character(EDUC_REFB)))
## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
levels(EDUC_REFB) <- c(FALSE,TRUE)
EDUC_REFB <- as.logical(EDUC_REFB)
na.omit(EDUC_REFB)
## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
model_temp2 <- lm(FWAGEXM ~ AGE_REF + I(AGE_REF^2) + I(REF_RACEB * SEX_REF) + SEX_REF + REF_RACE + EDUC_REF + CUTENURE + MARITAL1, data = dat_use) 
## Warning in REF_RACEB * SEX_REF: longer object length is not a multiple of
## shorter object length
summary(model_temp2)
## 
## Call:
## lm(formula = FWAGEXM ~ AGE_REF + I(AGE_REF^2) + I(REF_RACEB * 
##     SEX_REF) + SEX_REF + REF_RACE + EDUC_REF + CUTENURE + MARITAL1, 
##     data = dat_use)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -183320  -40797   -9609   27303  513902 
## 
## Coefficients:
##                                            Estimate Std. Error t value Pr(>|t|)
## (Intercept)                              -33834.200  21708.354  -1.559 0.119181
## AGE_REF                                    6463.718    589.986  10.956  < 2e-16
## I(AGE_REF^2)                                -66.044      6.152 -10.735  < 2e-16
## I(REF_RACEB * SEX_REF)                    -1753.541   1831.562  -0.957 0.338427
## SEX_REF                                   -6785.838   2293.716  -2.958 0.003111
## REF_RACEBlack                             -9596.594   4110.661  -2.335 0.019619
## REF_RACENative American                   -3739.446  11471.742  -0.326 0.744465
## REF_RACEAsian                              3830.872   4151.552   0.923 0.356194
## REF_RACEPacific Islander                   3923.299  15995.658   0.245 0.806259
## REF_RACEMulti Race                        26067.047   7611.269   3.425 0.000622
## EDUC_REFGrade 8                          -36112.116  17892.346  -2.018 0.043632
## EDUC_REFHigh School No Degree            -15203.739  17316.309  -0.878 0.380000
## EDUC_REFHigh School                       -7907.180  16987.902  -0.465 0.641630
## EDUC_REFSome College                      -2772.606  17005.168  -0.163 0.870492
## EDUC_REFAssociates                         1487.724  17164.122   0.087 0.930933
## EDUC_REFBachelors                         36271.987  17000.472   2.134 0.032942
## EDUC_REFMasters or PHD                    62204.465  17103.112   3.637 0.000280
## CUTENURENo Mortgage                      -28347.694   3564.162  -7.954 2.39e-15
## CUTENUREMortgage Status Not Reported     -16378.927  10032.770  -1.633 0.102650
## CUTENURERenter                           -32315.659   2778.991 -11.629  < 2e-16
## CUTENUREOccupied Without Payment Of Cash -26638.492  12037.567  -2.213 0.026962
## MARITAL1Widowed                          -22959.211   7528.770  -3.050 0.002308
## MARITAL1Divorced                         -31122.917   4069.526  -7.648 2.59e-14
## MARITAL1Seperated                        -36613.101   6993.784  -5.235 1.74e-07
## MARITAL1Never Married                    -12741.408   3969.143  -3.210 0.001338
##                                             
## (Intercept)                                 
## AGE_REF                                  ***
## I(AGE_REF^2)                             ***
## I(REF_RACEB * SEX_REF)                      
## SEX_REF                                  ** 
## REF_RACEBlack                            *  
## REF_RACENative American                     
## REF_RACEAsian                               
## REF_RACEPacific Islander                    
## REF_RACEMulti Race                       ***
## EDUC_REFGrade 8                          *  
## EDUC_REFHigh School No Degree               
## EDUC_REFHigh School                         
## EDUC_REFSome College                        
## EDUC_REFAssociates                          
## EDUC_REFBachelors                        *  
## EDUC_REFMasters or PHD                   ***
## CUTENURENo Mortgage                      ***
## CUTENUREMortgage Status Not Reported        
## CUTENURERenter                           ***
## CUTENUREOccupied Without Payment Of Cash *  
## MARITAL1Widowed                          ** 
## MARITAL1Divorced                         ***
## MARITAL1Seperated                        ***
## MARITAL1Never Married                    ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66990 on 3709 degrees of freedom
## Multiple R-squared:  0.2954, Adjusted R-squared:  0.2908 
## F-statistic: 64.78 on 24 and 3709 DF,  p-value: < 2.2e-16
plot(model_temp2)

require(stargazer)
stargazer(model_temp2, type = "default")
## 
## % Error: 'style' must be either 'latex' (default), 'html' or 'text.'
pick_use1 <- (Homeowner_data$AGE_REF >= 20) & (Homeowner_data$AGE_REF <= 87) & (Homeowner_data$FAM_SIZE >= 3)
dat_use1 <- subset(Homeowner_data,pick_use1) 
model_logit1 <- glm(CUTENURE ~ AGE_REF + I(AGE_REF^2) + EDUC_REF + REF_RACE + I(EDUC_REFB*SEX_REF) + SEX_REF, family = binomial, data = dat_use1)
## Warning in EDUC_REFB * SEX_REF: longer object length is not a multiple of
## shorter object length
summary(model_logit1)
## 
## Call:
## glm(formula = CUTENURE ~ AGE_REF + I(AGE_REF^2) + EDUC_REF + 
##     REF_RACE + I(EDUC_REFB * SEX_REF) + SEX_REF, family = binomial, 
##     data = dat_use1)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.3277  -0.9437  -0.7322   1.1178   1.8034  
## 
## Coefficients:
##                                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    4.5757383  0.7190797   6.363 1.97e-10 ***
## AGE_REF                       -0.1757775  0.0183767  -9.565  < 2e-16 ***
## I(AGE_REF^2)                   0.0018033  0.0001931   9.341  < 2e-16 ***
## EDUC_REFGrade 8                0.3206109  0.6284311   0.510 0.609928    
## EDUC_REFHigh School No Degree -0.2705750  0.6041829  -0.448 0.654271    
## EDUC_REFHigh School           -0.8313496  0.5919403  -1.404 0.160185    
## EDUC_REFSome College          -1.5195697  0.5921347  -2.566 0.010280 *  
## EDUC_REFAssociates            -1.7119625  0.5968185  -2.868 0.004124 ** 
## EDUC_REFBachelors             -1.8968497  0.5918159  -3.205 0.001350 ** 
## EDUC_REFMasters or PHD        -1.9634921  0.5953018  -3.298 0.000973 ***
## REF_RACEBlack                  0.5843422  0.1278914   4.569 4.90e-06 ***
## REF_RACENative American        0.8141263  0.3691101   2.206 0.027409 *  
## REF_RACEAsian                  0.6358050  0.1301226   4.886 1.03e-06 ***
## REF_RACEPacific Islander       2.0199211  0.7579092   2.665 0.007696 ** 
## REF_RACEMulti Race             0.5241916  0.2461737   2.129 0.033225 *  
## I(EDUC_REFB * SEX_REF)         0.0756228  0.0644693   1.173 0.240794    
## SEX_REF                        0.2630499  0.0727039   3.618 0.000297 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 5119.6  on 3733  degrees of freedom
## Residual deviance: 4620.6  on 3717  degrees of freedom
## AIC: 4654.6
## 
## Number of Fisher Scoring iterations: 4
nw_data2<- data.frame(AGE_REF=20:87, REF_RACE = "Black", EDUC_REF = "Bachelors", CUTENURE = "Mortgage", CUTENURE = "No Mortgage", CUTENURE = "Mortgage Status Not Reported", SEX_REF = 2)
nw_data2$yhat<-predict(model_logit1, nw_data2, type="response")
## Warning in EDUC_REFB * SEX_REF: longer object length is not a multiple of
## shorter object length
plot(nw_data2$yhat ~nw_data2$AGE , pch = 16, ylim = c(0,1.5), main = "Homeownership rate", xlab = "Age", ylab = "Percentage increase for meeting all the required variables", col = "green")

coefplot(model_logit1, innerCI=2, outerCI=0, intercept = FALSE, title = "Logit Model", color = "blue", lab = "Explantory Variables")